Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations75
Missing cells68
Missing cells (%)6.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.6 KiB
Average record size in memory322.8 B

Variable types

Categorical3
DateTime1
Numeric11

Alerts

Cancelled Wagers is highly overall correlated with Federal Excise Tax (4) and 10 other fieldsHigh correlation
Federal Excise Tax (4) is highly overall correlated with Cancelled Wagers and 9 other fieldsHigh correlation
Fiscal Year is highly overall correlated with NotesHigh correlation
Licensee is highly overall correlated with Cancelled Wagers and 10 other fieldsHigh correlation
Notes is highly overall correlated with Cancelled Wagers and 3 other fieldsHigh correlation
Online Sports Wagering Win/(Loss) is highly overall correlated with Cancelled Wagers and 9 other fieldsHigh correlation
Patron Winnings is highly overall correlated with Cancelled Wagers and 9 other fieldsHigh correlation
Payment (7) is highly overall correlated with Cancelled Wagers and 9 other fieldsHigh correlation
Promotional Coupons or Credits Wagered (5) is highly overall correlated with Cancelled Wagers and 9 other fieldsHigh correlation
Promotional Deduction (6) is highly overall correlated with Cancelled Wagers and 10 other fieldsHigh correlation
Total Gross Gaming Revenue is highly overall correlated with Cancelled Wagers and 9 other fieldsHigh correlation
Unadjusted Monthly Gaming Revenue is highly overall correlated with Cancelled Wagers and 9 other fieldsHigh correlation
Wagers is highly overall correlated with Cancelled Wagers and 9 other fieldsHigh correlation
Notes has 68 (90.7%) missing values Missing
Licensee is uniformly distributed Uniform
Wagers has unique values Unique
Patron Winnings has unique values Unique
Cancelled Wagers has unique values Unique
Monthly Resettlements (3) has unique values Unique
Online Sports Wagering Win/(Loss) has unique values Unique
Federal Excise Tax (4) has unique values Unique
Unadjusted Monthly Gaming Revenue has unique values Unique
Promotional Coupons or Credits Wagered (5) has unique values Unique
Promotional Deduction (6) has unique values Unique
Total Gross Gaming Revenue has unique values Unique
Payment (7) has unique values Unique

Reproduction

Analysis started2025-05-09 07:18:55.098082
Analysis finished2025-05-09 07:19:09.748593
Duration14.65 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Licensee
Categorical

High correlation  Uniform 

Distinct3
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
MPI Master Wagering License CT, LLC
25 
Mohegan Digital, LLC
25 
CT Lottery Corp
25 

Length

Max length35
Median length20
Mean length23.333333
Min length15

Characters and Unicode

Total characters1750
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMPI Master Wagering License CT, LLC
2nd rowMohegan Digital, LLC
3rd rowCT Lottery Corp
4th rowMPI Master Wagering License CT, LLC
5th rowMohegan Digital, LLC

Common Values

ValueCountFrequency (%)
MPI Master Wagering License CT, LLC 25
33.3%
Mohegan Digital, LLC 25
33.3%
CT Lottery Corp 25
33.3%

Length

2025-05-09T12:49:09.905457image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T12:49:10.034886image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
ct 50
16.7%
llc 50
16.7%
master 25
8.3%
mpi 25
8.3%
license 25
8.3%
wagering 25
8.3%
mohegan 25
8.3%
digital 25
8.3%
lottery 25
8.3%
corp 25
8.3%

Most occurring characters

ValueCountFrequency (%)
225
12.9%
e 150
 
8.6%
L 150
 
8.6%
C 125
 
7.1%
g 100
 
5.7%
r 100
 
5.7%
a 100
 
5.7%
t 100
 
5.7%
i 100
 
5.7%
n 75
 
4.3%
Other values (14) 525
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
225
12.9%
e 150
 
8.6%
L 150
 
8.6%
C 125
 
7.1%
g 100
 
5.7%
r 100
 
5.7%
a 100
 
5.7%
t 100
 
5.7%
i 100
 
5.7%
n 75
 
4.3%
Other values (14) 525
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
225
12.9%
e 150
 
8.6%
L 150
 
8.6%
C 125
 
7.1%
g 100
 
5.7%
r 100
 
5.7%
a 100
 
5.7%
t 100
 
5.7%
i 100
 
5.7%
n 75
 
4.3%
Other values (14) 525
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
225
12.9%
e 150
 
8.6%
L 150
 
8.6%
C 125
 
7.1%
g 100
 
5.7%
r 100
 
5.7%
a 100
 
5.7%
t 100
 
5.7%
i 100
 
5.7%
n 75
 
4.3%
Other values (14) 525
30.0%

Fiscal Year
Categorical

High correlation 

Distinct3
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2022/23
36 
2021/22
27 
2023/24
12 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters525
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023/24
2nd row2023/24
3rd row2023/24
4th row2023/24
5th row2023/24

Common Values

ValueCountFrequency (%)
2022/23 36
48.0%
2021/22 27
36.0%
2023/24 12
 
16.0%

Length

2025-05-09T12:49:10.174744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T12:49:10.288816image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
2022/23 36
48.0%
2021/22 27
36.0%
2023/24 12
 
16.0%

Most occurring characters

ValueCountFrequency (%)
2 288
54.9%
0 75
 
14.3%
/ 75
 
14.3%
3 48
 
9.1%
1 27
 
5.1%
4 12
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 288
54.9%
0 75
 
14.3%
/ 75
 
14.3%
3 48
 
9.1%
1 27
 
5.1%
4 12
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 288
54.9%
0 75
 
14.3%
/ 75
 
14.3%
3 48
 
9.1%
1 27
 
5.1%
4 12
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 288
54.9%
0 75
 
14.3%
/ 75
 
14.3%
3 48
 
9.1%
1 27
 
5.1%
4 12
 
2.3%
Distinct25
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
Minimum2021-10-31 00:00:00
Maximum2023-10-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-09T12:49:10.398282image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:10.522446image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

Wagers
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40039836
Minimum3246536
Maximum88261046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-05-09T12:49:10.672180image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum3246536
5-th percentile8860034.1
Q113664534
median41063793
Q360771806
95-th percentile74278564
Maximum88261046
Range85014510
Interquartile range (IQR)47107272

Descriptive statistics

Standard deviation23958829
Coefficient of variation (CV)0.59837481
Kurtosis-1.3777643
Mean40039836
Median Absolute Deviation (MAD)25192806
Skewness0.045229716
Sum3.0029877 × 109
Variance5.740255 × 1014
MonotonicityNot monotonic
2025-05-09T12:49:10.818226image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88261046 1
 
1.3%
75578037 1
 
1.3%
13331951 1
 
1.3%
79064255 1
 
1.3%
75742477 1
 
1.3%
15417664 1
 
1.3%
37127221 1
 
1.3%
37621550 1
 
1.3%
10162805 1
 
1.3%
39969518 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
3246536 1
1.3%
8504508 1
1.3%
8512696 1
1.3%
8518887 1
1.3%
9006240 1
1.3%
9298914 1
1.3%
9480658 1
1.3%
9728070 1
1.3%
9777896 1
1.3%
9988853 1
1.3%
ValueCountFrequency (%)
88261046 1
1.3%
79064255 1
1.3%
75742477 1
1.3%
75578037 1
1.3%
73721647 1
1.3%
73377779 1
1.3%
72443757 1
1.3%
69131840 1
1.3%
69000492 1
1.3%
68869666 1
1.3%

Patron Winnings
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36030915
Minimum2901339
Maximum80492445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-05-09T12:49:10.957662image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2901339
5-th percentile8113641
Q112838074
median36116631
Q355438338
95-th percentile66521124
Maximum80492445
Range77591106
Interquartile range (IQR)42600264

Descriptive statistics

Standard deviation21485771
Coefficient of variation (CV)0.59631489
Kurtosis-1.3535142
Mean36030915
Median Absolute Deviation (MAD)21770642
Skewness0.063854425
Sum2.7023186 × 109
Variance4.6163837 × 1014
MonotonicityNot monotonic
2025-05-09T12:49:11.088959image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80492445 1
 
1.3%
66242159 1
 
1.3%
11960580 1
 
1.3%
72235151 1
 
1.3%
66066300 1
 
1.3%
14480088 1
 
1.3%
33247151 1
 
1.3%
33644709 1
 
1.3%
9166153 1
 
1.3%
35703399 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
2901339 1
1.3%
7560957 1
1.3%
7620868 1
1.3%
7660972 1
1.3%
8307642 1
1.3%
8395848 1
1.3%
8508929 1
1.3%
8790467 1
1.3%
8930482 1
1.3%
9049691 1
1.3%
ValueCountFrequency (%)
80492445 1
1.3%
72235151 1
1.3%
67349094 1
1.3%
67172043 1
1.3%
66242159 1
1.3%
66066300 1
1.3%
65207806 1
1.3%
64101091 1
1.3%
62906213 1
1.3%
62213083 1
1.3%

Cancelled Wagers
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180422.28
Minimum11400
Maximum888312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-05-09T12:49:11.238894image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum11400
5-th percentile18518
Q154024
median150176
Q3225835
95-th percentile452849.2
Maximum888312
Range876912
Interquartile range (IQR)171811

Descriptive statistics

Standard deviation167676.17
Coefficient of variation (CV)0.92935399
Kurtosis6.8312509
Mean180422.28
Median Absolute Deviation (MAD)94998
Skewness2.2052509
Sum13531671
Variance2.8115297 × 1010
MonotonicityNot monotonic
2025-05-09T12:49:11.373414image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
159833 1
 
1.3%
114432 1
 
1.3%
25745 1
 
1.3%
191177 1
 
1.3%
219048 1
 
1.3%
37899 1
 
1.3%
196620 1
 
1.3%
193465 1
 
1.3%
45952 1
 
1.3%
198949 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
11400 1
1.3%
11418 1
1.3%
14674 1
1.3%
17510 1
1.3%
18950 1
1.3%
23778 1
1.3%
25745 1
1.3%
26918 1
1.3%
30092 1
1.3%
31622 1
1.3%
ValueCountFrequency (%)
888312 1
1.3%
881477 1
1.3%
509831 1
1.3%
506360 1
1.3%
429916 1
1.3%
380201 1
1.3%
378368 1
1.3%
358976 1
1.3%
340402 1
1.3%
320444 1
1.3%

Monthly Resettlements (3)
Real number (ℝ)

Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25199.36
Minimum-264510
Maximum370199
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)22.7%
Memory size732.0 B
2025-05-09T12:49:11.506326image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-264510
5-th percentile-53867.5
Q190
median10471
Q346798.5
95-th percentile132901
Maximum370199
Range634709
Interquartile range (IQR)46708.5

Descriptive statistics

Standard deviation70275.425
Coefficient of variation (CV)2.7887782
Kurtosis10.723238
Mean25199.36
Median Absolute Deviation (MAD)17960
Skewness0.88206583
Sum1889952
Variance4.9386354 × 109
MonotonicityNot monotonic
2025-05-09T12:49:11.638611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-16787 1
 
1.3%
51957 1
 
1.3%
6502 1
 
1.3%
-264510 1
 
1.3%
-72833 1
 
1.3%
5776 1
 
1.3%
-11512 1
 
1.3%
58189 1
 
1.3%
9550 1
 
1.3%
-220 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
-264510 1
1.3%
-109352 1
1.3%
-72833 1
1.3%
-69005 1
1.3%
-47380 1
1.3%
-28271 1
1.3%
-16787 1
1.3%
-11512 1
1.3%
-6865 1
1.3%
-4971 1
1.3%
ValueCountFrequency (%)
370199 1
1.3%
168505 1
1.3%
166787 1
1.3%
146530 1
1.3%
127060 1
1.3%
112645 1
1.3%
108792 1
1.3%
87270 1
1.3%
72442 1
1.3%
65525 1
1.3%

Online Sports Wagering Win/(Loss)
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3803298.8
Minimum293732
Maximum9529961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-05-09T12:49:11.781796image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum293732
5-th percentile596382.6
Q1966650.5
median3454517
Q36057848.5
95-th percentile8570190.3
Maximum9529961
Range9236229
Interquartile range (IQR)5091198

Descriptive statistics

Standard deviation2741909.6
Coefficient of variation (CV)0.72092932
Kurtosis-1.012803
Mean3803298.8
Median Absolute Deviation (MAD)2513367
Skewness0.41662359
Sum2.8524741 × 108
Variance7.5180683 × 1012
MonotonicityNot monotonic
2025-05-09T12:49:11.930982image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7625553 1
 
1.3%
9169489 1
 
1.3%
1339123 1
 
1.3%
6902437 1
 
1.3%
9529961 1
 
1.3%
893901 1
 
1.3%
3694961 1
 
1.3%
3725189 1
 
1.3%
941150 1
 
1.3%
4067391 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
293732 1
1.3%
443848 1
1.3%
444003 1
1.3%
550359 1
1.3%
616107 1
1.3%
663650 1
1.3%
761722 1
1.3%
776387 1
1.3%
782302 1
1.3%
790424 1
1.3%
ValueCountFrequency (%)
9529961 1
1.3%
9259168 1
1.3%
9210202 1
1.3%
9169489 1
1.3%
8313348 1
1.3%
8099159 1
1.3%
8089935 1
1.3%
7625553 1
1.3%
7398294 1
1.3%
7390714 1
1.3%

Federal Excise Tax (4)
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96960.6
Minimum8013
Maximum212331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-05-09T12:49:12.088659image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum8013
5-th percentile22072.8
Q134036
median98380
Q3146991
95-th percentile179861.3
Maximum212331
Range204318
Interquartile range (IQR)112955

Descriptive statistics

Standard deviation57604.259
Coefficient of variation (CV)0.59409966
Kurtosis-1.3856668
Mean96960.6
Median Absolute Deviation (MAD)59931
Skewness0.050419436
Sum7272045
Variance3.3182507 × 109
MonotonicityNot monotonic
2025-05-09T12:49:12.233002image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212331 1
 
1.3%
181797 1
 
1.3%
33265 1
 
1.3%
188874 1
 
1.3%
181472 1
 
1.3%
38449 1
 
1.3%
90348 1
 
1.3%
91098 1
 
1.3%
25292 1
 
1.3%
98021 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
8013 1
1.3%
21047 1
1.3%
21193 1
1.3%
21279 1
1.3%
22413 1
1.3%
23118 1
1.3%
23623 1
1.3%
24205 1
1.3%
24293 1
1.3%
24834 1
1.3%
ValueCountFrequency (%)
212331 1
1.3%
188874 1
1.3%
181797 1
1.3%
181472 1
1.3%
179171 1
1.3%
177370 1
1.3%
175023 1
1.3%
169448 1
1.3%
168668 1
1.3%
167935 1
1.3%

Unadjusted Monthly Gaming Revenue
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3706338.2
Minimum285720
Maximum9348489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-05-09T12:49:12.388475image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum285720
5-th percentile562831.6
Q1936374
median3303530
Q35904383.5
95-th percentile8399231.7
Maximum9348489
Range9062769
Interquartile range (IQR)4968009.5

Descriptive statistics

Standard deviation2691570.2
Coefficient of variation (CV)0.72620738
Kurtosis-0.99743109
Mean3706338.2
Median Absolute Deviation (MAD)2416998
Skewness0.42900565
Sum2.7797537 × 108
Variance7.24455 × 1012
MonotonicityNot monotonic
2025-05-09T12:49:12.525561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7413222 1
 
1.3%
8987693 1
 
1.3%
1305858 1
 
1.3%
6713563 1
 
1.3%
9348489 1
 
1.3%
855452 1
 
1.3%
3604613 1
 
1.3%
3634091 1
 
1.3%
915858 1
 
1.3%
3969369 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
285720 1
1.3%
412332 1
1.3%
421591 1
1.3%
518082 1
1.3%
582010 1
1.3%
630320 1
1.3%
740443 1
1.3%
748048 1
1.3%
758009 1
1.3%
769377 1
1.3%
ValueCountFrequency (%)
9348489 1
1.3%
9091233 1
1.3%
9049544 1
1.3%
8987693 1
1.3%
8147034 1
1.3%
7949569 1
1.3%
7940030 1
1.3%
7413222 1
1.3%
7282851 1
1.3%
7231959 1
1.3%

Promotional Coupons or Credits Wagered (5)
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1093995.7
Minimum164324
Maximum2996029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-05-09T12:49:12.675190image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum164324
5-th percentile205847.2
Q1302536
median883084
Q31849372.5
95-th percentile2580134.9
Maximum2996029
Range2831705
Interquartile range (IQR)1546836.5

Descriptive statistics

Standard deviation832511.88
Coefficient of variation (CV)0.76098277
Kurtosis-0.88069387
Mean1093995.7
Median Absolute Deviation (MAD)615978
Skewness0.62221504
Sum82049678
Variance6.9307603 × 1011
MonotonicityNot monotonic
2025-05-09T12:49:12.808152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2517905 1
 
1.3%
2744995 1
 
1.3%
260578 1
 
1.3%
2996029 1
 
1.3%
2934685 1
 
1.3%
289925 1
 
1.3%
648713 1
 
1.3%
989043 1
 
1.3%
233468 1
 
1.3%
451851 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
164324 1
1.3%
171541 1
1.3%
181008 1
1.3%
193355 1
1.3%
211201 1
1.3%
218504 1
1.3%
218616 1
1.3%
219855 1
1.3%
232747 1
1.3%
233468 1
1.3%
ValueCountFrequency (%)
2996029 1
1.3%
2934685 1
1.3%
2744995 1
1.3%
2725338 1
1.3%
2517905 1
1.3%
2398934 1
1.3%
2257935 1
1.3%
2244890 1
1.3%
2199505 1
1.3%
2194575 1
1.3%

Promotional Deduction (6)
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean758716
Minimum71430
Maximum1987392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-05-09T12:49:12.937747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum71430
5-th percentile128483.2
Q1199528
median759271
Q31166175
95-th percentile1762107
Maximum1987392
Range1915962
Interquartile range (IQR)966647

Descriptive statistics

Standard deviation556788.97
Coefficient of variation (CV)0.7338569
Kurtosis-0.9080185
Mean758716
Median Absolute Deviation (MAD)520049
Skewness0.50307925
Sum56903700
Variance3.1001396 × 1011
MonotonicityNot monotonic
2025-05-09T12:49:13.192428image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1111983 1
 
1.3%
1348154 1
 
1.3%
195879 1
 
1.3%
1342713 1
 
1.3%
1869698 1
 
1.3%
171090 1
 
1.3%
648713 1
 
1.3%
726818 1
 
1.3%
183172 1
 
1.3%
451851 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
71430 1
1.3%
103083 1
1.3%
105398 1
1.3%
126064 1
1.3%
129520 1
1.3%
145503 1
1.3%
153875 1
1.3%
164324 1
1.3%
166985 1
1.3%
168670 1
1.3%
ValueCountFrequency (%)
1987392 1
1.3%
1869698 1
1.3%
1809909 1
1.3%
1787671 1
1.3%
1751151 1
1.3%
1749925 1
1.3%
1705889 1
1.3%
1629407 1
1.3%
1588006 1
1.3%
1540559 1
1.3%

Total Gross Gaming Revenue
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2947622.2
Minimum214290
Maximum7639539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-05-09T12:49:13.341036image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum214290
5-th percentile422123.9
Q1718457.5
median2615404
Q34735385.5
95-th percentile6734229.4
Maximum7639539
Range7425249
Interquartile range (IQR)4016928

Descriptive statistics

Standard deviation2162807.1
Coefficient of variation (CV)0.73374638
Kurtosis-0.91421717
Mean2947622.2
Median Absolute Deviation (MAD)1909880
Skewness0.46392171
Sum2.2107166 × 108
Variance4.6777346 × 1012
MonotonicityNot monotonic
2025-05-09T12:49:13.538532image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6301239 1
 
1.3%
7639539 1
 
1.3%
1109979 1
 
1.3%
5370850 1
 
1.3%
7478791 1
 
1.3%
684362 1
 
1.3%
2955900 1
 
1.3%
2907273 1
 
1.3%
732686 1
 
1.3%
3517518 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
214290 1
1.3%
309249 1
1.3%
316193 1
1.3%
388561 1
1.3%
436508 1
1.3%
504256 1
1.3%
555332 1
1.3%
561036 1
1.3%
568507 1
1.3%
609530 1
1.3%
ValueCountFrequency (%)
7639539 1
1.3%
7478791 1
1.3%
7341308 1
1.3%
7239635 1
1.3%
6517627 1
1.3%
6352024 1
1.3%
6301239 1
1.3%
5962177 1
1.3%
5826281 1
1.3%
5785567 1
1.3%

Payment (7)
Real number (ℝ)

High correlation  Unique 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405298.04
Minimum29465
Maximum1050437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size732.0 B
2025-05-09T12:49:13.681028image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum29465
5-th percentile58042.1
Q198788
median359618
Q3651115.5
95-th percentile925956.8
Maximum1050437
Range1020972
Interquartile range (IQR)552327.5

Descriptive statistics

Standard deviation297386.01
Coefficient of variation (CV)0.73374647
Kurtosis-0.9142163
Mean405298.04
Median Absolute Deviation (MAD)262608
Skewness0.46392215
Sum30397353
Variance8.8438437 × 1010
MonotonicityNot monotonic
2025-05-09T12:49:13.821885image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
866420 1
 
1.3%
1050437 1
 
1.3%
152622 1
 
1.3%
738492 1
 
1.3%
1028334 1
 
1.3%
94100 1
 
1.3%
406436 1
 
1.3%
399750 1
 
1.3%
100744 1
 
1.3%
483659 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
29465 1
1.3%
42522 1
1.3%
43477 1
1.3%
53427 1
1.3%
60020 1
1.3%
69335 1
1.3%
76358 1
1.3%
77142 1
1.3%
78170 1
1.3%
83810 1
1.3%
ValueCountFrequency (%)
1050437 1
1.3%
1028334 1
1.3%
1009430 1
1.3%
995450 1
1.3%
896174 1
1.3%
873403 1
1.3%
866420 1
1.3%
819799 1
1.3%
801114 1
1.3%
795516 1
1.3%

Notes
Categorical

High correlation  Missing 

Distinct3
Distinct (%)42.9%
Missing68
Missing (%)90.7%
Memory size4.2 KiB
8
9
8,9

Length

Max length3
Median length1
Mean length1.2857143
Min length1

Characters and Unicode

Total characters9
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)28.6%

Sample

1st row9
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 5
 
6.7%
9 1
 
1.3%
8,9 1
 
1.3%
(Missing) 68
90.7%

Length

2025-05-09T12:49:13.966237image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T12:49:14.087608image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
8 5
71.4%
9 1
 
14.3%
8,9 1
 
14.3%

Most occurring characters

ValueCountFrequency (%)
8 6
66.7%
9 2
 
22.2%
, 1
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 6
66.7%
9 2
 
22.2%
, 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 6
66.7%
9 2
 
22.2%
, 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 6
66.7%
9 2
 
22.2%
, 1
 
11.1%

Interactions

2025-05-09T12:49:07.933156image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:55.813212image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.992166image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:58.196327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:59.242399image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:00.725038image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:01.775865image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:03.070978image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:04.257067image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:05.474141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:06.715997image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:08.018910image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.015291image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:57.078708image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:58.276090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:59.410199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:00.802079image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:01.894159image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:03.177998image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:04.340152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:05.567292image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:06.827370image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:08.109849image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.122124image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:57.169105image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:58.377393image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:59.599673image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:00.910574image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:02.007037image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:03.261670image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:04.452517image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:05.673514image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:06.936462image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:08.196141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.210549image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:57.260019image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:58.458531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:59.776933image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:00.994050image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:02.131185image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:03.373952image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:04.542384image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:05.785707image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:07.028036image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:08.290801image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.313465image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:57.380032image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:58.542123image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:59.876509image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:01.087823image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:02.240627image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:03.521540image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:04.651610image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:05.891263image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:07.174152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:08.371995image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.395108image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:57.491923image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:58.634399image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:59.959798image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:01.178132image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:02.362314image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:03.650708image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:04.746122image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:06.002638image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:07.260829image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:08.474395image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.503212image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:57.651855image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:58.767870image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:00.091366image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:01.288140image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:02.494085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:03.767216image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:04.849491image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:06.135920image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:07.380252image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:08.555847image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.577532image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:57.727340image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:58.844344image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:00.328063image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:01.366738image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:02.594003image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:03.845871image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:04.937097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:06.258910image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:07.457574image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:08.654604image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.678297image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:57.827664image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:58.943326image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:00.429113image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:01.476445image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:02.727910image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:03.951384image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:05.039104image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:06.359142image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:07.558810image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:08.759879image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.779164image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:57.959251image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:59.042831image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:00.538475image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:01.579558image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:02.841371image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:04.053608image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:05.292299image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:06.474317image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:07.719366image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:08.866911image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:56.894832image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:58.075141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:48:59.161756image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:00.627032image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:01.692819image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:02.963978image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:04.158624image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:05.398786image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:06.608754image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-05-09T12:49:07.826334image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2025-05-09T12:49:14.173859image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Cancelled WagersFederal Excise Tax (4)Fiscal YearLicenseeMonthly Resettlements (3)NotesOnline Sports Wagering Win/(Loss)Patron WinningsPayment (7)Promotional Coupons or Credits Wagered (5)Promotional Deduction (6)Total Gross Gaming RevenueUnadjusted Monthly Gaming RevenueWagers
Cancelled Wagers1.0000.6070.1820.6010.2220.8660.6200.6040.6240.5360.6500.6240.6190.613
Federal Excise Tax (4)0.6071.0000.3150.6430.2800.2240.8710.9970.8630.8270.8580.8630.8670.999
Fiscal Year0.1820.3151.0000.0000.1411.0000.0000.2490.1490.2270.0000.1490.0000.291
Licensee0.6010.6430.0001.0000.2211.0000.6630.6460.6760.6850.6660.6760.6640.643
Monthly Resettlements (3)0.2220.2800.1410.2211.0000.0000.1440.2910.1430.1760.1380.1430.1400.268
Notes0.8660.2241.0001.0000.0001.0000.0000.2240.0000.3870.6320.0000.0000.224
Online Sports Wagering Win/(Loss)0.6200.8710.0000.6630.1440.0001.0000.8430.9970.8110.9680.9971.0000.875
Patron Winnings0.6040.9970.2490.6460.2910.2240.8431.0000.8330.8210.8370.8330.8390.996
Payment (7)0.6240.8630.1490.6760.1430.0000.9970.8331.0000.7870.9531.0000.9970.866
Promotional Coupons or Credits Wagered (5)0.5360.8270.2270.6850.1760.3870.8110.8210.7871.0000.8620.7870.8060.832
Promotional Deduction (6)0.6500.8580.0000.6660.1380.6320.9680.8370.9530.8621.0000.9530.9670.864
Total Gross Gaming Revenue0.6240.8630.1490.6760.1430.0000.9970.8331.0000.7870.9531.0000.9970.866
Unadjusted Monthly Gaming Revenue0.6190.8670.0000.6640.1400.0001.0000.8390.9970.8060.9670.9971.0000.871
Wagers0.6130.9990.2910.6430.2680.2240.8750.9960.8660.8320.8640.8660.8711.000

Missing values

2025-05-09T12:49:09.010520image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-09T12:49:09.259180image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LicenseeFiscal YearMonth EndingWagersPatron WinningsCancelled WagersMonthly Resettlements (3)Online Sports Wagering Win/(Loss)Federal Excise Tax (4)Unadjusted Monthly Gaming RevenuePromotional Coupons or Credits Wagered (5)Promotional Deduction (6)Total Gross Gaming RevenuePayment (7)Notes
0MPI Master Wagering License CT, LLC2023/2410/31/2023 12:00:00 AM8826104680492445159833-1678776255532123317413222251790511119836301239866420NaN
1Mohegan Digital, LLC2023/2410/31/2023 12:00:00 AM755780376624215911443251957916948918179789876932744995134815476395391050437NaN
2CT Lottery Corp2023/2410/31/2023 12:00:00 AM133319511196058025745650213391233326513058582605781958791109979152622NaN
3MPI Master Wagering License CT, LLC2023/2409/30/2023 12:00:00 AM7906425572235151191177-26451069024371888746713563299602913427135370850738492NaN
4Mohegan Digital, LLC2023/2409/30/2023 12:00:00 AM7574247766066300219048-72833952996118147293484892934685186969874787911028334NaN
5CT Lottery Corp2023/2409/30/2023 12:00:00 AM15417664144800883789957768939013844985545228992517109068436294100NaN
6MPI Master Wagering License CT, LLC2023/2408/31/2023 12:00:00 AM3712722133247151196620-1151236949619034836046136487136487132955900406436NaN
7Mohegan Digital, LLC2023/2408/31/2023 12:00:00 AM37621550336447091934655818937251899109836340919890437268182907273399750NaN
8CT Lottery Corp2023/2408/31/2023 12:00:00 AM10162805916615345952955094115025292915858233468183172732686100744NaN
9MPI Master Wagering License CT, LLC2023/2407/31/2023 12:00:00 AM3996951835703399198949-22040673919802139693694518514518513517518483659NaN
LicenseeFiscal YearMonth EndingWagersPatron WinningsCancelled WagersMonthly Resettlements (3)Online Sports Wagering Win/(Loss)Federal Excise Tax (4)Unadjusted Monthly Gaming RevenuePromotional Coupons or Credits Wagered (5)Promotional Deduction (6)Total Gross Gaming RevenuePayment (7)Notes
65CT Lottery Corp2021/2201/31/2022 12:00:00 AM136503041300953611400132616161073409758201028959514550343650860020NaN
66MPI Master Wagering License CT, LLC2021/2212/31/2021 12:00:00 AM6886966664101091358976-4407441400616557742484292027294106210731863224381198
67Mohegan Digital, LLC2021/2212/31/2021 12:00:00 AM6331549759439866224237103973640998153793348720515740878718012615404359618NaN
68CT Lottery Corp2021/2212/31/2021 12:00:00 AM11385256105423176376827847763872833974804826080518701256103677142NaN
69MPI Master Wagering License CT, LLC2021/2211/30/2021 12:00:00 AM600310245287016516499428431696743414387768235572045623170588951176677036798
70Mohegan Digital, LLC2021/2211/30/2021 12:00:00 AM58999340507511071275503074880899351403667949569272533819873925962177819799NaN
71CT Lottery Corp2021/2211/30/2021 12:00:00 AM8518887766097299459-32677617222127974044340525318511155533276358NaN
72MPI Master Wagering License CT, LLC2021/2210/31/2021 12:00:00 AM237702502011708643488-41213613797534273560370211944989009226702773671638,9
73Mohegan Digital, LLC2021/2210/31/2021 12:00:00 AM27082842260470821027655883487416061453812707239893420317760953083810NaN
74CT Lottery Corp2021/2210/31/2021 12:00:00 AM32465362901339414181004729373280132857204044317143021429029465NaN